
Covariance Prediction via Convex Optimization
We consider the problem of predicting the covariance of a zero mean Gaus...
read it

Low Rank Forecasting
We consider the problem of forecasting multiple values of the future of ...
read it

Learning Convex Optimization Models
A convex optimization model predicts an output from an input by solving ...
read it

Optimal Representative Sample Weighting
We consider the problem of assigning weights to a set of samples or data...
read it

Convex Optimization Over RiskNeutral Probabilities
We consider a collection of derivatives that depend on the price of an u...
read it

Learning Convex Optimization Control Policies
Many control policies used in various applications determine the input o...
read it

Differentiable Convex Optimization Layers
Recent work has shown how to embed differentiable optimization problems ...
read it

Minimizing a Sum of Clipped Convex Functions
We consider the problem of minimizing a sum of clipped convex functions;...
read it

A Distributed Method for Fitting Laplacian Regularized Stratified Models
Stratified models are models that depend in an arbitrary way on a set of...
read it

Least Squares AutoTuning
Least squares is by far the simplest and most commonly applied computati...
read it

Learning Probabilistic Trajectory Models of Aircraft in Terminal Airspace from Position Data
Models for predicting aircraft motion are an important component of mode...
read it

Improved Training with Curriculum GANs
In this paper we introduce Curriculum GANs, a curriculum learning strate...
read it

Optimizing for Generalization in Machine Learning with CrossValidation Gradients
Crossvalidation is the workhorse of modern applied statistics and machi...
read it

Cooperative MultiAgent Reinforcement Learning for LowLevel Wireless Communication
Traditional radio systems are strictly codesigned on the lower levels o...
read it

A Note on the Inception Score
Deep generative models are powerful tools that have produced impressive ...
read it

Active Robotic Mapping through Deep Reinforcement Learning
We propose an approach to learning agents for active robotic mapping, wh...
read it

InterpNET: Neural Introspection for Interpretable Deep Learning
Humans are able to explain their reasoning. On the contrary, deep neural...
read it
Shane Barratt
is this you? claim profile